Merge branch 'TP2_mardi' of https://gitlab.utc.fr/DataVenture/api-h19 into TP2_mardi

TP/TP2_mardi/TP_Regression.ipynb

@@ -4,8 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Regression\n",
-    "Rappel problème de régression"
+    "# TP Apprentissage supervisé: Régression\n",
+    "Dans ce TP, on va faire la regression. C'est pour analyser la relation d'une variable par rapport à une ou plusieurs autres."
    ]
   },
   {
@@ -19,32 +19,56 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "On va utiliser les données Boston.\n",
+    "https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html\n",
+    "\n",
+    "Prix des maisons à Boston (cf le site pour les variables)\n",
+    "https://scikit-learn.org/stable/datasets/index.html#boston-dataset\n",
+    "\n",
+    "Importez les libraries de ce matin: `numpy` et `scikit datasets`.\n",
     "Consultation de la doc du dataset\n",
+    "\n",
     "Chargement du dataset boston"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Analyse exploratoire et préparation du dataset\n",
-    "Étudier les corrélations"
+    "Étudier les corrélations en utilisant `np.corrcoef`"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
-   "source": [
-    "Split du dataset boston"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##"
+    "Split du dataset boston\n",
+    "\n",
+    "Pour cela, utilisez la fonction scikit-learn `sklearn.model_selection.train_test_split`. Importez cette méthode, "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -54,6 +78,13 @@
     "Trouver le modèle sur scikit learn."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -61,15 +92,28 @@
     "Run sur boston. afficher les coef de chaque features. Quelles features sont significative?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Arbre de régression\n",
-    "Rappel modèle\n",
-    "image"
+    "![](https://fr.wikipedia.org/wiki/Arbre_de_d%C3%A9cision#/media/File:Arbre_de_decision.jpg)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -77,6 +121,13 @@
     "Essayer avec une profondeur max de 3"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -84,6 +135,13 @@
     "Essayer avec une profondeur max de 5"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -91,6 +149,13 @@
     "Essayer avec une profondeur max de 10"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -98,6 +163,13 @@
     "Comparer les résultats"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -108,6 +180,13 @@
     "modèle"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -115,6 +194,13 @@
     "Essayer avec 3 arbres"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -122,6 +208,13 @@
     "Essayer avec 10 arbres"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -129,6 +222,13 @@
     "100 arbres"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -136,6 +236,13 @@
     "Comparer avec les arbres de régression. Quels sont les avantages?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -143,6 +250,13 @@
     "_optionel_ Tracer le résultat avec 1 arbre, 3 arbres et 100 arbres "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -152,6 +266,13 @@
     "\n",
     "Faire une régression sur le résultat d'une PCA (touchy)\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -170,7 +291,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.7.2"
   }
  },
  "nbformat": 4,